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Journal of Engineering Science and Technology Special Issue on SOMCHE 2014 & RSCE 2014 Conference, January (2015) 25 - 34 © School of Engineering, Taylor’s University

25

A FRAMEWORK FOR SOLVENT SELECTION BASED ON HERBAL EXTRACTION PROCESS DESIGN

S. N. H. M. AZMIN1, N. A. YUNUS

1,

A. A. MUSTAFFA1, S. R. WAN ALWI

1,*, L. S. CHUA

2

1Process Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering,

Universiti Teknologi Malaysia, 81310 UTM, Johor Bahru, Malaysia 2Institute of Bioproduct Development (IBD), Universiti Teknologi Malaysia,

81310 UTM, Johor Bahru, Malaysia *Corresponding Author: [email protected]

Abstract

In the extraction of Malaysian herbs phytochemical, the uses of solvents are

very important as a transfer medium. Most of the current studies are only

focusing on the effect of using different solvent types, different solvent to herbs ratio, effect of phytochemicals to the scavenging activity, antioxidant property

and so on. There are very limited literatures on solvent blended design methods

for phytochemicals extraction from herbs. Practically, different solvent can only

extract certain phytochemicals that have the same number of partition

coefficient. In this study, both solvents and phytochemicals properties are

concerned in order to design a blended solvent that can enhance the extraction

process and extract the optimum amount of phytochemicals from herbs. In

addition, the safety, economic and environmental aspects of the solvent are also

considered. The main objective of this work is to design solvent blends for the

extraction of herbal phytochemicals using computer-aided approach. The

methodology is divided into four tasks which are Task (I): Problem definition, Task (II): Property model identification, Task (III): Design solvent blend and

Task (IV): Model based verification. In this paper, only Task (I) up to Task (III)

is illustrated for calculation. Task (IV) will be presented in future paper. This

proposed method has been applied to design a solvent mixture for the extraction

of Kaempferol from Kacip Fatimah herb as a case study. From the analysis, 17

feasible binary solvents mixture have been identified suitable for the extraction as it was within range of the design target.

Keywords: Extraction, Product design, Solvent blend, Phytochemicals, Kacip

Fatimah.

26 S. N. H. Mohammad Azmin et al.

Journal of Engineering Science and Technology Special Issue 1 1/2015

Nomenclatures ζ

k Targets properties

ζLB Target values lower bound

ζUB Target values upper bound

ζSk Solvent target value

ζik,m

Pure solvent property k of compound i in the mixture m

xi mole fraction of compound i

��,��,�

Lower bound composition for a binary mixture, m

��,��,�

Upper bound composition for a binary mixture, m

Log Kow Partition coefficient Tb Boiling point log LC50 Toxicity parameter

Greek Symbols µ Viscosity ρ Density δ Solubility parameter

Activity coefficient

∆Hfus, Heat of fusion

Tmi Melting point

Abbreviations

PI Performance Index

1. Introduction

Extractions of Malaysian herbs have been widely done by other researchers.

However, from the literatures, it was found that limited study has been done on the

relationship between the solvent properties to the phytochemicals properties. Most

of the researchers only focus on the extraction yield by using different solvent,

effect of solvent-Malaysian herbs ratio to the extraction yield, effect of

phytochemicals to the scavenging activity, antioxidant property and so on.

As an example, Karimi, Jaafar [1] investigated the total extraction yield in

Labisia Pumila (Kacip Fatimah) and its antimicrobial activities. Filly, Fernandez [2]

combined two methods (microwave heating and distillation) to increase the

extraction yield which is the essential oil of Rosmarinus Officinalis L. (Rosemary).

Meanwhile, Konar, Dalabasmaz [3] determined the caffeic acid derivatives of

Echinacea purpurea aerial parts under various supercritical fluid extraction (SFE)

conditions.

The main issue in solvent selection used in herbal extraction is the decisions are

based on trial-and-error method. Traditional methods mainly focused on

experimental analysis by clasifying solvent (polarity) for different solute [4]. This

method is however time and resource intensive [5]. In addition, to extract one

phytochemical, at least six solvents are needed. The reduction of solvents needed

can reduce the cost and time, as well as increase or maintain productivity [6]. The

combination of property predictive models with computer-assisted search [5] is one

way to reduce experiments needed which to be conducted.

A Framework for Solvent Selection Based on Herbal Extraction Process Design 27


From the literature review, no reported publication has been found on a

framework for selecting the suitable solvent blend that can extract the optimum

phytochemicals from Malaysian herbs by using computer-aided approach. Some

relevant literature such as Karunanithi et al. [1] designed an optimal

extractant/solvent for the separation of acetic acid from water by using Liquid-

Liquid Extraction (LLE), Conte et al. [2] and Conte et al. [3] designed a solvent

blend for paint and insect repellent formulation and Yunus et al. [4] designed a

framework for blending gasoline and lubricant base oils for production of green

gasoline. Thus, the objective of this research is to develop a systematic and generic

approach to design solvent blends that could lead to reduction of solvent waste,

extraction time and cost, and increase the phytochemicals extraction yield

production for herbs.

2. Methodology

In product-process design reverse approach is used somewhere else [5], where

there are two stages involved. The first stage is to define the design target while

the second stage is to identify the alternatives that match the target. This approach

was applied for the reason of all processes depends on some key properties of the

product and on the effect of these properties on the process performance [6]. In the

first stage, product performance target are set and relevant property models are

identified. In the second stage, appropriate models are used to identify a list of

products which matches the target properties.

Figure 1 shows a systematic methodology for designing a solvent blend. As

shown in Fig. 1, Task (I) and (II) follow the first stage and Task (III) and (IV)

follow the second stage. Task (IV) is needed to prove that the listed solvent blends can

be used in the selected process as it is verified with the experimental result.

Fig. 1. Systematic Methodology for Designing Blended Solvent.



2.1. Tasks of the methodology

2.1.1. Task (I): Problem definition

Task (I) is used to define the problems in matching the user needs. The goal for

this task is to get the properties related to the selected process and to set the

boundary for the selected property. In this task, the mechanism in the selected

process will be considered to identify the requirements to improve the process and

the factor that can increase the process efficiency. All requirements and factors

will be listed in this study. The important needs that have been listed will then be

translated into performance criteria. From these performance criteria, the

properties related to the selected process will be considered in the study and listed

down. In this step, constraints of the listed properties are also specified. All the

constraints are set depending on the literature search and existing specified

constraints for the selected process.

2.1.2. Task (II): Property model identification

The goal for Task (II) is to find the suitable property model for the selected

process. For this task, the property models from the literature are collected and

tested on its suitability with the selected process. The pure target properties could

be predicted from group contribution method while the mixture target properties

could be predicted using the property models from literature if available. The most

suitable model will be used to predict the properties of single and blended solvents.

2.1.3. Task (III): Design solvent blend

The next task is to design the solvent blend. First, the database of the solvent

candidates and phytochemicals must be selected. The database needed is the

phytochemicals list with their properties, and a list of chemicals/solvents with

their associated properties. The potential solvent blends that match all the

properties constraints set in Task (II) are then listed. Through this task, the goal of

finding the optimal solvent blend can be obtained.

2.1.3.1. Mixture design algorithm

Mixture Design Algorithm is applied to design the solvent mixtures by matching

the constraints of the listed properties. The outputs of this task will be a mixture

that matches the targeted composition, cost, target property value and the solvent

blend stability as shown in Fig. 2.

2.1.3.1.(a) Level 1: Pure component constraints

At this level, the pure component properties of solvent in the database and target

phytochemicals are compared with respect to the target values. The aim for this

level is to get the list of pure solvent that match the phytochemical target

properties value. The targets properties, ζk

of the solvent in the database are

compared with the target properties, ζk of each phytochemicals and with the target

values boundaries, ζLB and ζUB. The solvents are rejected if the property value of

the solvent are either lower than the lower bound values (ζSk<ζLB

k) or greater than

the upper bound values (ζSk>ζUB

k).



2.1.3.1.(b) Level 2: Linear design constraints

Binary mixture screening of solvent is starting at this level. Linear constraints are

related to the properties described by linear models. In this case study, linear

models are following the linear mixing rule to compute the mixture target

property. For binary mixture, the generic form of the linear model is:

�,� = � � ��

�,�=��.

�,�+ �1 − � ��. �

�,� (1)

In this equation, subscript 1 and 2 indicates solvent 1 and 2 in the binary

mixture; ζik,m is the pure solvent property k of compound i in the mixture m; xi is

the mole fraction of compound i. In this step, the composition boundaries for each

target properties of solvent in binary mixture are calculated using the equation 2.

�,� is a specific target value for property k.

��,�

=��,��

�,�

��,�

��,� (2)

The composition range of solvent 1, (��,��,�

and ��,��,�

) for a binary mixture, m

is calculated as follows based on eqs. (3) and (4):

��,��,�

=��

�

��

� (3)

��,��,�

=��! ��

�

��

� (4)

The overall composition range (��,��,�

and ��,��,�

) for each mixture is set by

comparing the composition range of all target properties. The minimum and

maximum values of ��,��,�

and ��,��,�

are calculated by eqs. (5) and (6) for each

property k used as follows:

��,�� = max��,��

�,�� (5)

��,�� = max��,��

�,�� (6)

The solvent mixture with the composition range of each property which does

not overlap each other is rejected.

2.1.3.1.(c) Level 3:Non-linear design constraints

At the end of level 2, binary mixtures candidates with their compositions

boundary have been determined. In this third level, non-linear constraints are

applied for further screening of the solvent mixtures. For this step, the non-linear

mixture properties, �,� for the remaining binary mixtures and new composition

ranges which satisfy the non-linear constraints are determined. The mixtures are

rejected for which the calculated property values do not match the non-linear

property constraints.

2.1.3.1.(d) Level 4: Phase stability constraints

At the end of level 3, mixtures that do not satisfied the target properties are

rejected. In this level, the mixture’s stability is determined, where the unstable



mixtures will be rejected. Phase split should not occur between the binary mixture

candidates. The result from this stability step is either the mixture (binary pairs) is

totally miscible, partially miscible or immiscible. The mixtures showing phase

split at the design composition are rejected.

2.1.3.1.(e) Level 5: Cost and extraction yield calculations

This level is divided into two parts which are cost calculation (Level 5A) and

extraction yield calculation (Level 5B).

In level 5A, the binary mixtures with their composition are evaluated with the

solvent price. Meanwhile in level 5B, the binary mixtures with their composition

are also evaluated for phytochemical compositions in extraction yield. In this

level, both cost and extraction yield can be calculated simultaneously. The results

are then combined to get the optimum phytochemical yield with the lowest cost

solvent used in extraction. The remaining binary solvent mixtures are ranked

according to increasing cost and extraction yield.

2.1.4. Task (IV): Model-based verification

The verification task is to ensure that the target properties of the candidate solvent

mixtures estimated with rigorous models are satisfying the constraints. If the

mixture properties are not in the target properties constraints, the listed solvent

blend candidates will be rejected. In this paper, Task (IV) will not be presented.

3. Case Study: Solvent Blend for Extracting Kaempferol (Phyto-

chemical) from Kacip Fatimah Herb

The aim of this case study is to design a solvent blend that can maximise the

extraction of Kaempferol, which is one of main phytochemical in Kacip Fatimah

herb. The blend solvent formulation is considered for non-consumable

phytochemicals product. The solvent blend that will be designed are considered to

be used for the conventional extraction and the temperature considered is 90 oC as

experimental run by Karimi et al (2011)[7]. 30 solvents data were used consisting of

alcohol, hydrocarbon, ether and ester solvent categories. The main phytochemicals

in Kacip Fatimah have been identified to be Kaempferol, Myricetin, Quercetin and

Rutin. In this paper, the case study followed the systematic methodology in Fig. 1,

from Task (I) to Task (III). In Task (III), the case study is only analysed from level

1 until level 3 (non-linear design constraints).

3.1. Task (I): Problem definition

Following are the main characteristics which have been identified from

knowledge base for the selection of solvent blends for phytochemical extraction

from herb: can effectively extract the selected phytochemicals from herb, can be

removed from the solvent and crude extract mixture, have low toxicity, must be

miscible to each other, low price and good solvent appearance. According to the

knowledge base, the solvent desired characteristics needs to be translated into

target properties. Referring to the existing solvent used in extraction process and

consulting the knowledge base, the constraints corresponding to the target

properties were defined as stated in Table 1.



3.2. Task (II): Property model identification

The target properties which are partition coefficient (log kow), toxicity parameter

(LC50), solubility parameter (δ), viscosity (µ), density (ρ) and cost (C) are

estimated by using linear mixing rules while the others are predicted by using

non-linear models. The linear mixing model is represented by Eq. (7).

� = � � ��

� (7)

Table 1. Target Property Constraints for Herb Solvent Blends Design.

Table 2 shows the model used for the target properties of blend solvent.

Table 2. List of Blend Target Properties and Models used in this Work.

Target property Model

Partition coefficient, Log Kow Linear mixing rule

Boiling point, Tb Klein et al (1992)[8]

Toxicity, LC50 Linear mixing rule

Stability, ∆Gmix Pinal et al (1991)[9]

Solubility parameter, δ Linear mixing rule Viscosity, µ Mehrotra et al (1996)[10]

Density, ρ [Yunus et al (2014)[11]]

3.3. Task (III): Design solvent blend

Current practice uses a mixture of alcohol and water as solvent in extracting

phytochemicals from herbs. The normal solvent used are ethanol, methanol and

propanol blend with water. The mixture composition is determined through trial-

and-error method or from the literature. 30 solvents data and four main

phytochemicals for Kacip Fatimah with their selected properties are used as input

data. The data are listed in Table 3.

Table 3. Input Data for Phytochemicals.

Property

Phytochemical δ, Mpa1/2 Tb,K Log kow ∆Hfus, Kj/mol Tm,K

Kaempferol 30.25 728.4 1.69 50.551 505.7

Myricetin 39.18 765.8 1.15 63.509 541.9

Quercetin 35.18 747.5 1.44 57.03 523.4

Rutin 89.040 878.4 -1.08 120.233 581.3

Target property value

Property Solvent constraints Phytochemical

constraints

Partition coefficient Log Kow(depends on phytochemicals) -0.3 ≤Log Kow≤ 4.44

Boiling point 333.15 K ≤Tb ≤ 348.15 K -

Toxicity parameter -2.5 ≤-log LC50≤ 2.5 -

Viscosity 1.20 cP ≤ µ ≤1.24cP -

Density 1.0g/cm3 ≤ρ≤ 1.5g/cm3 -

Solubility parameter δ ( depends on phytochemicals) 16≤ δ≤48 Mpa1/2



3.3.1. Mixture design algorithm

In this study, Kaempferol which is one of the main phytochemical in Kacip

Fatimah herb has been selected as the desired phytochemical to be extracted.

3.3.1.(a) Level 1: Pure component constraints

In this level, two properties which are solubility parameter and partition

coefficient are considered. These properties have the interrelation between solvent

and phytochemicals which affect the extraction process efficiency. The other

properties used are for safety and compatibility to the extraction process

consideration. After considering all the constraints set in level 1, from the 870

(total combination of binary solvents = (n-1) x n, where n is number of solvent in

database) possible total combinations of binary solvents, 119 binary solvents

combinations have been screened which satisfy all the constraints specified in

level 1. These binary solvents combination will be further screened in level 2.

3.3.1.(b) Level 2: Linear design constraints

Linear properties which are partition coefficient, solubility parameter, density and

toxicity are considered in this level. The composition range that matched the

target properties boundary is obtained, followed by the determination of overall

composition range for each mixture candidates. From this level, the solvent

mixture candidates that matched the target properties are 36 combinations.

3.3.1.(c) Level 3: Non-linear design constraints

Non-linear property which is boiling point is now considered. Fig. 2 shows the

reduction number of solvent blend combination from Level 1 to Level 3.

From this figure, the decomposition method shows the reduction of solvent

mixture candidates in order to match the target property constraints. Therefore,

this method can be used to search the optimal solvent blends that match the target

properties values.

After the non-linear property model is calculated, the list of candidates will be

further reduced. In this level, the composition of the binary mixture listed in Level

2 was used to find the value of boiling point that match the target property as

stated in Table 1. Boiling point value that did not match the target value was

removed. The overall composition range that matched the boiling point is obtained.

There are 17 feasible solvent mixtures after the non-linear design constraint with the

properties that satisfy all the target properties are listed in Table 4.

In this table, the listed solvent mixtures (S1+S2) with solvent compositions, x1 are

selected based on the target properties listed in Table 1. As an example, the solubility

parameter, δ for target solvent mixture is between 16 Mpa1/2 and 48 Mpa1/2. Thus,

the solvent mixture listed in Table 4 must have the solubility parameter value within

this range. The same goes to other properties which are partition coefficient (log kow),

density (ρ), toxicity (-log LC50), and boiling point, (Tb).



Fig. 2. The Reduction Number of Solvent

Blend Combination from Level 1 to Level 3.

Table 4. Feasible Solvent Mixtures with their Properties.

Mixture

S1+S2

x1 log

kow

δ

Mpa1/2

Ρ

g/cm3

-log

LC50

µ

cP

Tb

K

Methanol+water 0.75 -0.17 28.48 1.36 2.13 1.20 340.65

Methanol+water 0.76 -0.17 28.22 1.37 2.15 1.20 340.65

Methanol+water 0.77 -0.18 27.97 1.37 2.16 1.21 340.65 Methanol+water 0.78 -0.18 27.71 1.38 2.18 1.21 340.65

Methanol+water 0.79 -0.18 27.45 1.38 2.20 1.22 340.65

Methanol+water 0.8 -0.18 27.19 1.39 2.21 1.22 340.65

Methanol+water 0.81 -0.19 26.93 1.39 2.23 1.22 340.65

Methanol+water 0.82 -0.19 26.68 1.40 2.24 1.23 340.65

Methanol+water 0.83 -0.06 26.42 1.40 2.26 1.23 340.65

Methanol+Etyhl

acetate 0.92 -0.15 21.72 1.44 2.12 1.21 333.65

Methanol+Etyhl

acetate 0.93 -0.16 21.76 1.44 2.17 1.22 333.65

Methanol+Etyhl acetate

0.94 -0.17 21.80 1.45 2.22 1.23 333.65

Methanol+acetic

acid 0.45 -0.15 19.49 1.18 -0.07 1.21 346.35

Methanol+acetic

acid 0.53 -0.16 19.86 1.22 0.31 1.22 337.65

Methanol+acetic

acid 0.55 -0.16 19.95 1.23 0.40 1.23 338.55

Methanol+acetic

acid 0.61 -0.17 20.23 1.27 0.68 1.24 348.15

1,3-Propanediol-

2+metyhlpropanal 0.27 0.50 29.82 1.17 -0.45 1.21 347.15

4. Conclusions

A systematic methodology for design of blended solvent for extracting

phytochemicals from herb has been developed and was tested on the extraction of

Kaempferol phytochemical from Kacip Fatimah herb. A decomposition method has

been applied to solve the blending problem, where the objectives are to quickly

screen out a large number of alternatives and to reduce the search space at each

hierarchical step. The 17 shortlisted solvent blends in non-linear design constraints



needs to be checked on its stability and will be further verified with experimental

study. The methodology applied can be used to design blended solvent for

extracting phytochemicals from any herb where the scope and size depend on the

solvent data base available and models availability in the property model library.

For future work, this systematic methodology needs to be verified with different

herbs as case studies.

Acknowledgement

This work was supported by the Research University Grant, RUG (Vote number:

Q.J130000.2544.03H44) Universiti Teknologi Malaysia,UTM and the Ministry

of Education, Malaysia. This support is gratefully acknowledged.

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